CLAUDE.md, AGENTS.md, context compression, and memory management: giving agents the information they need.
Anthropic's applied AI team argues that context engineering is a continuation of prompt engineering, and the core idea is picking the smallest set of high-signal tokens within a limited attention budget.
Why it matters: Anthropic lays out a systematic approach to context engineering, covering the trade-offs among three long-task strategies: compression, note-taking, and sub-agents.
The author rewrote a long block of CLAUDE.md rules as a GraphViz dot flowchart, using quoted strings as node names, different shapes to distinguish decisions, commands, and warnings, and giving each flow an explicit trigger condition.
Why it matters: After rewriting the CLAUDE.md rules as a GraphViz dot flowchart, Claude followed the rules better, and this approach can be carried over to your own projects.
The Manus team shares context engineering lessons from building AI agents, centered on designing around the KV cache, managing tools by masking rather than removing them, treating the file system as context, steering attention by restating to-do items, keeping errors in context, and avoiding getting stuck on few-shot examples.
Why it matters: The Manus team distilled lessons from rewriting their agent framework four times into six context engineering principles—ready to apply directly to your own agent implementation.
In his article, Lance Martin groups context engineering for agents into four strategies: writing (using scratchpads and memory to store information outside the context window) and selecting (pulling in memory, tool descriptions, and knowledge on demand).
Why it matters: The article groups agent context management into four strategies—writing, selecting, compressing, and isolating—and shows how various products put them into practice, making it easy to compare against your existing workflow.